Learning active shape models for bifurcating contours

M. Seise, S. J. McKenna, I. W. Ricketts, C. A. Wigderowitz

    Research output: Contribution to journalArticlepeer-review

    16 Citations (Scopus)


    Statistical shape models are often learned from examples based on landmark correspondences between annotated examples. A method is proposed for learning such models from contours with inconsistent bifurcations and loops. Automatic segmentation of tibial and femoral contours in knee X-ray images is investigated as a step towards reliable, quantitative radiographic analysis of osteoarthritis for diagnosis and assessment of progression. Results are presented using various features, the Mahalanobis distance, distance weighted K-nearest neighbours, and two relevance vector machine-based methods as quality of fit measure
    Original languageEnglish
    Pages (from-to)666-677
    Number of pages12
    JournalIEEE Transactions on Medical Imaging
    Issue number5
    Publication statusPublished - May 2007


    • Active shape models
    • Image segmentation
    • Image shape analysis
    • Osteoarthritis
    • X-ray imaging


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